Разведочный анализ данных. Исследование и визуализация данных.

1) Текстовое описание набора данных

В качестве набора данных был выбран известный датасет бостонского жилья.

Описание колонок:

  • crim - per capita crime rate by town.

  • zn - proportion of residential land zoned for lots over 25,000 sq.ft.

  • indus - proportion of non-retail business acres per town.

  • chas - Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).

  • nox - nitrogen oxides concentration (parts per 10 million).

  • rm - average number of rooms per dwelling.

  • age - proportion of owner-occupied units built prior to 1940.

  • dis - weighted mean of distances to five Boston employment centres.

  • rad - index of accessibility to radial highways.

  • tax - full-value property-tax rate per \$10,000.

  • ptratio - pupil-teacher ratio by town.

  • black - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.

  • lstat - lower status of the population (percent).

  • medv - median value of owner-occupied homes in \$1000s.

Переменная medv является целевой.

Импорт библиотек

In [ ]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Загрузка данных

In [ ]:
data_path = 'data/Boston.csv'
df = pd.read_csv(data_path)

2) Основные характеристики датасета

Первые пять строчек

In [ ]:
df.head()
Out[ ]:
Unnamed: 0 crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
0 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0
1 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6
2 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7
3 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4
4 5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.2

Определим размер датасета

In [ ]:
df.shape
Out[ ]:
(506, 15)
In [ ]:
total_count = df.shape[0]
print('Всего строк: {}'.format(total_count))

Всего строк: 506
In [ ]:
columns = df.columns
print('Колонки: {}'.format(columns))

Колонки: Index(['Unnamed: 0', 'crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis',
       'rad', 'tax', 'ptratio', 'black', 'lstat', 'medv'],
      dtype='object')

Список колонок с типами данных:

In [ ]:
df.dtypes
Out[ ]:
Unnamed: 0      int64
crim          float64
zn            float64
indus         float64
chas            int64
nox           float64
rm            float64
age           float64
dis           float64
rad             int64
tax             int64
ptratio       float64
black         float64
lstat         float64
medv          float64
dtype: object

Проверка наличия пустых значений

In [ ]:
cols_with_nulls = []
for col in df.columns:
    # Количество пустых значений - все значения заполнены
    temp_null_count = df[df[col].isnull()].shape[0]
    if temp_null_count: cols_with_nulls.append(col)
    print('{} - {}'.format(col, temp_null_count))

if len(cols_with_nulls):
    print('Есть пустые значения')
else:
    print('Пустых значений нет')

Unnamed: 0 - 0
crim - 0
zn - 0
indus - 0
chas - 0
nox - 0
rm - 0
age - 0
dis - 0
rad - 0
tax - 0
ptratio - 0
black - 0
lstat - 0
medv - 0
Пустых значений нет

Основные статистические характеристки набора данных

In [ ]:
df.describe()
Out[ ]:
Unnamed: 0 crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000
mean 253.500000 3.613524 11.363636 11.136779 0.069170 0.554695 6.284634 68.574901 3.795043 9.549407 408.237154 18.455534 356.674032 12.653063 22.532806
std 146.213884 8.601545 23.322453 6.860353 0.253994 0.115878 0.702617 28.148861 2.105710 8.707259 168.537116 2.164946 91.294864 7.141062 9.197104
min 1.000000 0.006320 0.000000 0.460000 0.000000 0.385000 3.561000 2.900000 1.129600 1.000000 187.000000 12.600000 0.320000 1.730000 5.000000
25% 127.250000 0.082045 0.000000 5.190000 0.000000 0.449000 5.885500 45.025000 2.100175 4.000000 279.000000 17.400000 375.377500 6.950000 17.025000
50% 253.500000 0.256510 0.000000 9.690000 0.000000 0.538000 6.208500 77.500000 3.207450 5.000000 330.000000 19.050000 391.440000 11.360000 21.200000
75% 379.750000 3.677083 12.500000 18.100000 0.000000 0.624000 6.623500 94.075000 5.188425 24.000000 666.000000 20.200000 396.225000 16.955000 25.000000
max 506.000000 88.976200 100.000000 27.740000 1.000000 0.871000 8.780000 100.000000 12.126500 24.000000 711.000000 22.000000 396.900000 37.970000 50.000000

Определим уникальные значения для целевого признака - medv

In [ ]:
df['medv'].unique()
Out[ ]:
array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,
       21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 13.6, 19.6, 15.2, 14.5,
       15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 13.2, 13.1, 13.5, 20. ,
       24.7, 30.8, 34.9, 26.6, 25.3, 21.2, 19.3, 14.4, 19.4, 19.7, 20.5,
       25. , 23.4, 35.4, 31.6, 23.3, 18.7, 16. , 22.2, 33. , 23.5, 22. ,
       17.4, 20.9, 24.2, 22.8, 24.1, 21.4, 20.8, 20.3, 28. , 23.9, 24.8,
       22.5, 23.6, 22.6, 20.6, 28.4, 38.7, 43.8, 33.2, 27.5, 26.5, 18.6,
       20.1, 19.5, 19.8, 18.8, 18.5, 18.3, 19.2, 17.3, 15.7, 16.2, 18. ,
       14.3, 23. , 18.1, 17.1, 13.3, 17.8, 14. , 13.4, 11.8, 13.8, 14.6,
       15.4, 21.5, 15.3, 17. , 41.3, 24.3, 27. , 50. , 22.7, 23.8, 22.3,
       19.1, 29.4, 23.2, 24.6, 29.9, 37.2, 39.8, 37.9, 32.5, 26.4, 29.6,
       32. , 29.8, 37. , 30.5, 36.4, 31.1, 29.1, 33.3, 30.3, 34.6, 32.9,
       42.3, 48.5, 24.4, 22.4, 28.1, 23.7, 26.7, 30.1, 44.8, 37.6, 46.7,
       31.5, 31.7, 41.7, 48.3, 29. , 25.1, 17.6, 24.5, 26.2, 42.8, 21.9,
       44. , 36. , 33.8, 43.1, 48.8, 31. , 36.5, 30.7, 43.5, 20.7, 21.1,
       25.2, 35.2, 32.4, 33.1, 35.1, 45.4, 46. , 32.2, 28.5, 37.3, 27.9,
       28.6, 36.1, 28.2, 16.1, 22.1, 19. , 32.7, 31.2, 17.2, 16.8, 10.2,
       10.4, 10.9, 11.3, 12.3,  8.8,  7.2, 10.5,  7.4, 11.5, 15.1,  9.7,
       12.5,  8.5,  5. ,  6.3,  5.6, 12.1,  8.3, 11.9, 17.9, 16.3,  7. ,
        7.5,  8.4, 16.7, 14.2, 11.7, 11. ,  9.5, 14.1,  9.6,  8.7, 12.8,
       10.8, 14.9, 12.6, 13. , 16.4, 17.7, 12. , 21.8,  8.1])

3) Визуальное исследование датасета

Для визуального исследования могут быть использованы различные виды диаграмм, мы построим только некоторые варианты диаграмм, которые используются достаточно часто.

Диаграмма рассеяния

In [ ]:
fig, ax = plt.subplots(figsize=(10,10)) 
sns.scatterplot(ax=ax, x='rm', y='medv', data=df)
Out[ ]:
<AxesSubplot:xlabel='rm', ylabel='medv'>
In [ ]:
fig, ax = plt.subplots(figsize=(10,10)) 
sns.scatterplot(ax=ax, x='rm', y='medv', data=df, hue='age')
Out[ ]:
<AxesSubplot:xlabel='rm', ylabel='medv'>

Гистограмма

In [ ]:
fig, ax = plt.subplots(figsize=(10,10)) 
sns.distplot(df['rm'])

/Users/feelsbadmans/Univer/bmstu-6-sem-tmo/.venv/lib/python3.8/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
Out[ ]:
<AxesSubplot:xlabel='rm', ylabel='Density'>

Jointplot

In [ ]:
sns.jointplot(x='rm', y='medv', data=df)
Out[ ]:
<seaborn.axisgrid.JointGrid at 0x2b6cb4970>
In [ ]:
sns.jointplot(x='rm', y='medv', data=df, kind='hex')
Out[ ]:
<seaborn.axisgrid.JointGrid at 0x2b6e2ee50>
In [ ]:
sns.jointplot(x='rm', y='medv', data=df, kind='kde')
Out[ ]:
<seaborn.axisgrid.JointGrid at 0x2b6f89c10>

"Парные диаграммы"

In [ ]:
sns.pairplot(df)
Out[ ]:
<seaborn.axisgrid.PairGrid at 0x2b786e550>
In [ ]:
sns.pairplot(df, hue="medv")
Out[ ]:
<seaborn.axisgrid.PairGrid at 0x2c0259940>

Ящик с усами

In [ ]:
sns.boxplot(x=df['medv'])
Out[ ]:
<AxesSubplot:xlabel='medv'>
In [ ]:
sns.boxplot(y=df['medv'])
Out[ ]:
<AxesSubplot:ylabel='medv'>
In [ ]:
sns.boxplot(x='chas', y='medv', data=df)
Out[ ]:
<AxesSubplot:xlabel='chas', ylabel='medv'>

Violin plot

In [ ]:
sns.violinplot(x=df['medv'])
Out[ ]:
<AxesSubplot:xlabel='medv'>
In [ ]:
sns.violinplot(y=df['medv'])
Out[ ]:
<AxesSubplot:ylabel='medv'>
In [ ]:
sns.violinplot(x='chas', y='medv', data=df)
Out[ ]:
<AxesSubplot:xlabel='chas', ylabel='medv'>

4) Информация о корреляции признаков

In [ ]:
df.corr()
Out[ ]:
Unnamed: 0 crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
Unnamed: 0 1.000000 0.407407 -0.103393 0.399439 -0.003759 0.398736 -0.079971 0.203784 -0.302211 0.686002 0.666626 0.291074 -0.295041 0.258465 -0.226604
crim 0.407407 1.000000 -0.200469 0.406583 -0.055892 0.420972 -0.219247 0.352734 -0.379670 0.625505 0.582764 0.289946 -0.385064 0.455621 -0.388305
zn -0.103393 -0.200469 1.000000 -0.533828 -0.042697 -0.516604 0.311991 -0.569537 0.664408 -0.311948 -0.314563 -0.391679 0.175520 -0.412995 0.360445
indus 0.399439 0.406583 -0.533828 1.000000 0.062938 0.763651 -0.391676 0.644779 -0.708027 0.595129 0.720760 0.383248 -0.356977 0.603800 -0.483725
chas -0.003759 -0.055892 -0.042697 0.062938 1.000000 0.091203 0.091251 0.086518 -0.099176 -0.007368 -0.035587 -0.121515 0.048788 -0.053929 0.175260
nox 0.398736 0.420972 -0.516604 0.763651 0.091203 1.000000 -0.302188 0.731470 -0.769230 0.611441 0.668023 0.188933 -0.380051 0.590879 -0.427321
rm -0.079971 -0.219247 0.311991 -0.391676 0.091251 -0.302188 1.000000 -0.240265 0.205246 -0.209847 -0.292048 -0.355501 0.128069 -0.613808 0.695360
age 0.203784 0.352734 -0.569537 0.644779 0.086518 0.731470 -0.240265 1.000000 -0.747881 0.456022 0.506456 0.261515 -0.273534 0.602339 -0.376955
dis -0.302211 -0.379670 0.664408 -0.708027 -0.099176 -0.769230 0.205246 -0.747881 1.000000 -0.494588 -0.534432 -0.232471 0.291512 -0.496996 0.249929
rad 0.686002 0.625505 -0.311948 0.595129 -0.007368 0.611441 -0.209847 0.456022 -0.494588 1.000000 0.910228 0.464741 -0.444413 0.488676 -0.381626
tax 0.666626 0.582764 -0.314563 0.720760 -0.035587 0.668023 -0.292048 0.506456 -0.534432 0.910228 1.000000 0.460853 -0.441808 0.543993 -0.468536
ptratio 0.291074 0.289946 -0.391679 0.383248 -0.121515 0.188933 -0.355501 0.261515 -0.232471 0.464741 0.460853 1.000000 -0.177383 0.374044 -0.507787
black -0.295041 -0.385064 0.175520 -0.356977 0.048788 -0.380051 0.128069 -0.273534 0.291512 -0.444413 -0.441808 -0.177383 1.000000 -0.366087 0.333461
lstat 0.258465 0.455621 -0.412995 0.603800 -0.053929 0.590879 -0.613808 0.602339 -0.496996 0.488676 0.543993 0.374044 -0.366087 1.000000 -0.737663
medv -0.226604 -0.388305 0.360445 -0.483725 0.175260 -0.427321 0.695360 -0.376955 0.249929 -0.381626 -0.468536 -0.507787 0.333461 -0.737663 1.000000
In [ ]:
df.corr(method='pearson')
Out[ ]:
Unnamed: 0 crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
Unnamed: 0 1.000000 0.407407 -0.103393 0.399439 -0.003759 0.398736 -0.079971 0.203784 -0.302211 0.686002 0.666626 0.291074 -0.295041 0.258465 -0.226604
crim 0.407407 1.000000 -0.200469 0.406583 -0.055892 0.420972 -0.219247 0.352734 -0.379670 0.625505 0.582764 0.289946 -0.385064 0.455621 -0.388305
zn -0.103393 -0.200469 1.000000 -0.533828 -0.042697 -0.516604 0.311991 -0.569537 0.664408 -0.311948 -0.314563 -0.391679 0.175520 -0.412995 0.360445
indus 0.399439 0.406583 -0.533828 1.000000 0.062938 0.763651 -0.391676 0.644779 -0.708027 0.595129 0.720760 0.383248 -0.356977 0.603800 -0.483725
chas -0.003759 -0.055892 -0.042697 0.062938 1.000000 0.091203 0.091251 0.086518 -0.099176 -0.007368 -0.035587 -0.121515 0.048788 -0.053929 0.175260
nox 0.398736 0.420972 -0.516604 0.763651 0.091203 1.000000 -0.302188 0.731470 -0.769230 0.611441 0.668023 0.188933 -0.380051 0.590879 -0.427321
rm -0.079971 -0.219247 0.311991 -0.391676 0.091251 -0.302188 1.000000 -0.240265 0.205246 -0.209847 -0.292048 -0.355501 0.128069 -0.613808 0.695360
age 0.203784 0.352734 -0.569537 0.644779 0.086518 0.731470 -0.240265 1.000000 -0.747881 0.456022 0.506456 0.261515 -0.273534 0.602339 -0.376955
dis -0.302211 -0.379670 0.664408 -0.708027 -0.099176 -0.769230 0.205246 -0.747881 1.000000 -0.494588 -0.534432 -0.232471 0.291512 -0.496996 0.249929
rad 0.686002 0.625505 -0.311948 0.595129 -0.007368 0.611441 -0.209847 0.456022 -0.494588 1.000000 0.910228 0.464741 -0.444413 0.488676 -0.381626
tax 0.666626 0.582764 -0.314563 0.720760 -0.035587 0.668023 -0.292048 0.506456 -0.534432 0.910228 1.000000 0.460853 -0.441808 0.543993 -0.468536
ptratio 0.291074 0.289946 -0.391679 0.383248 -0.121515 0.188933 -0.355501 0.261515 -0.232471 0.464741 0.460853 1.000000 -0.177383 0.374044 -0.507787
black -0.295041 -0.385064 0.175520 -0.356977 0.048788 -0.380051 0.128069 -0.273534 0.291512 -0.444413 -0.441808 -0.177383 1.000000 -0.366087 0.333461
lstat 0.258465 0.455621 -0.412995 0.603800 -0.053929 0.590879 -0.613808 0.602339 -0.496996 0.488676 0.543993 0.374044 -0.366087 1.000000 -0.737663
medv -0.226604 -0.388305 0.360445 -0.483725 0.175260 -0.427321 0.695360 -0.376955 0.249929 -0.381626 -0.468536 -0.507787 0.333461 -0.737663 1.000000
In [ ]:
df.corr(method='kendall')
Out[ ]:
Unnamed: 0 crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
Unnamed: 0 1.000000 0.287507 -0.119015 0.210280 -0.003072 0.270223 -0.025624 0.131520 -0.214680 0.439464 0.360426 0.222774 -0.108716 0.159116 -0.170050
crim 0.287507 1.000000 -0.462057 0.521014 0.033948 0.603361 -0.211718 0.497297 -0.539878 0.563969 0.544956 0.312768 -0.264378 0.454837 -0.403964
zn -0.119015 -0.462057 1.000000 -0.535468 -0.039419 -0.511464 0.278134 -0.429389 0.478524 -0.234663 -0.289911 -0.361607 0.128177 -0.386818 0.339989
indus 0.210280 0.521014 -0.535468 1.000000 0.075889 0.612030 -0.291318 0.489070 -0.565137 0.353967 0.483228 0.336612 -0.192017 0.465980 -0.418430
chas -0.003072 0.033948 -0.039419 0.075889 1.000000 0.056387 0.048080 0.055616 -0.065619 0.021739 -0.037655 -0.115694 -0.033277 -0.041344 0.115202
nox 0.270223 0.603361 -0.511464 0.612030 0.056387 1.000000 -0.215633 0.589608 -0.683930 0.434828 0.453258 0.278678 -0.202430 0.452005 -0.394995
rm -0.025624 -0.211718 0.278134 -0.291318 0.048080 -0.215633 1.000000 -0.187611 0.179801 -0.076569 -0.190532 -0.223194 0.032951 -0.468231 0.482829
age 0.131520 0.497297 -0.429389 0.489070 0.055616 0.589608 -0.187611 1.000000 -0.609836 0.306201 0.360311 0.251857 -0.154056 0.485359 -0.387758
dis -0.214680 -0.539878 0.478524 -0.565137 -0.065619 -0.683930 0.179801 -0.609836 1.000000 -0.361892 -0.381988 -0.223486 0.168631 -0.409347 0.313115
rad 0.439464 0.563969 -0.234663 0.353967 0.021739 0.434828 -0.076569 0.306201 -0.361892 1.000000 0.558107 0.251913 -0.214364 0.287943 -0.248115
tax 0.360426 0.544956 -0.289911 0.483228 -0.037655 0.453258 -0.190532 0.360311 -0.381988 0.558107 1.000000 0.287769 -0.241606 0.384191 -0.414650
ptratio 0.222774 0.312768 -0.361607 0.336612 -0.115694 0.278678 -0.223194 0.251857 -0.223486 0.251913 0.287769 1.000000 -0.042152 0.330335 -0.398789
black -0.108716 -0.264378 0.128177 -0.192017 -0.033277 -0.202430 0.032951 -0.154056 0.168631 -0.214364 -0.241606 -0.042152 1.000000 -0.145430 0.126955
lstat 0.159116 0.454837 -0.386818 0.465980 -0.041344 0.452005 -0.468231 0.485359 -0.409347 0.287943 0.384191 0.330335 -0.145430 1.000000 -0.668656
medv -0.170050 -0.403964 0.339989 -0.418430 0.115202 -0.394995 0.482829 -0.387758 0.313115 -0.248115 -0.414650 -0.398789 0.126955 -0.668656 1.000000
In [ ]:
df.corr(method='spearman')
Out[ ]:
Unnamed: 0 crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
Unnamed: 0 1.000000 0.461037 -0.160505 0.324621 -0.003759 0.432492 -0.035641 0.208323 -0.373499 0.588481 0.536928 0.297897 -0.154474 0.257542 -0.273633
crim 0.461037 1.000000 -0.571660 0.735524 0.041537 0.821465 -0.309116 0.704140 -0.744986 0.727807 0.729045 0.465283 -0.360555 0.634760 -0.558891
zn -0.160505 -0.571660 1.000000 -0.642811 -0.041937 -0.634828 0.361074 -0.544423 0.614627 -0.278767 -0.371394 -0.448475 0.163135 -0.490074 0.438179
indus 0.324621 0.735524 -0.642811 1.000000 0.089841 0.791189 -0.415301 0.679487 -0.757080 0.455507 0.664361 0.433710 -0.285840 0.638747 -0.578255
chas -0.003759 0.041537 -0.041937 0.089841 1.000000 0.068426 0.058813 0.067792 -0.080248 0.024579 -0.044486 -0.136065 -0.039810 -0.050575 0.140612
nox 0.432492 0.821465 -0.634828 0.791189 0.068426 1.000000 -0.310344 0.795153 -0.880015 0.586429 0.649527 0.391309 -0.296662 0.636828 -0.562609
rm -0.035641 -0.309116 0.361074 -0.415301 0.058813 -0.310344 1.000000 -0.278082 0.263168 -0.107492 -0.271898 -0.312923 0.053660 -0.640832 0.633576
age 0.208323 0.704140 -0.544423 0.679487 0.067792 0.795153 -0.278082 1.000000 -0.801610 0.417983 0.526366 0.355384 -0.228022 0.657071 -0.547562
dis -0.373499 -0.744986 0.614627 -0.757080 -0.080248 -0.880015 0.263168 -0.801610 1.000000 -0.495806 -0.574336 -0.322041 0.249595 -0.564262 0.445857
rad 0.588481 0.727807 -0.278767 0.455507 0.024579 0.586429 -0.107492 0.417983 -0.495806 1.000000 0.704876 0.318330 -0.282533 0.394322 -0.346776
tax 0.536928 0.729045 -0.371394 0.664361 -0.044486 0.649527 -0.271898 0.526366 -0.574336 0.704876 1.000000 0.453345 -0.329843 0.534423 -0.562411
ptratio 0.297897 0.465283 -0.448475 0.433710 -0.136065 0.391309 -0.312923 0.355384 -0.322041 0.318330 0.453345 1.000000 -0.072027 0.467259 -0.555905
black -0.154474 -0.360555 0.163135 -0.285840 -0.039810 -0.296662 0.053660 -0.228022 0.249595 -0.282533 -0.329843 -0.072027 1.000000 -0.210562 0.185664
lstat 0.257542 0.634760 -0.490074 0.638747 -0.050575 0.636828 -0.640832 0.657071 -0.564262 0.394322 0.534423 0.467259 -0.210562 1.000000 -0.852914
medv -0.273633 -0.558891 0.438179 -0.578255 0.140612 -0.562609 0.633576 -0.547562 0.445857 -0.346776 -0.562411 -0.555905 0.185664 -0.852914 1.000000
In [ ]:
sns.heatmap(df.corr())
Out[ ]:
<AxesSubplot:>
In [ ]:
sns.heatmap(df.corr(), annot=True, fmt='.1f')
Out[ ]:
<AxesSubplot:>
In [ ]:
sns.heatmap(df.corr(), annot=True, fmt='.1f', cmap='YlGnBu')
Out[ ]:
<AxesSubplot:>
In [ ]:
mask = np.zeros_like(df.corr(), dtype=np.bool)
mask[np.tril_indices_from(mask)] = True
sns.heatmap(df.corr(), mask=mask, annot=True, fmt='.1f')

/var/folders/k8/94s33qgs47b0y9dc3zwr8t240000gn/T/ipykernel_62257/1732128229.py:1: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  mask = np.zeros_like(df.corr(), dtype=np.bool)
Out[ ]:
<AxesSubplot:>
In [ ]:
fig, ax = plt.subplots(1, 3, sharex='col', sharey='row', figsize=(24,5))
sns.heatmap(df.corr(method='pearson'), ax=ax[0], annot=True, fmt='.1f')
sns.heatmap(df.corr(method='kendall'), ax=ax[1], annot=True, fmt='.1f')
sns.heatmap(df.corr(method='spearman'), ax=ax[2], annot=True, fmt='.1f')
fig.suptitle('Корреляционные матрицы, построенные различными методами')
ax[0].title.set_text('Pearson')
ax[1].title.set_text('Kendall')
ax[2].title.set_text('Spearman')